R Graphics with ggplot2
Evan Muzzall
November 4, 2019
options(width = 200)1 Resources for data visualization
Wickham et al. 2019. ggplot2
Wilke CO. 2019. Introduction to cowplot
Wilke CO. 2019. Arranging plots in a grid
Tufte ER. 2001. The Visual Display of Quantitative Information
Wilke CO. 2019. Fundamentals of Data Visualization
Wilkinson L. 1999. The Grammar of Graphics
1.0.1 Plotting in R
There are two major sets of tools for creating plots in R:
- base graphics, which come with all R installations
- base graphics, which come with all R installations
- ggplot2, a stand-alone package.
Note that other plotting facilities do exist (notably lattice), but base and ggplot2 are by far the most popular. Check out this post on comparisons between base, lattice, and ggplot2 graphics to learn more.
1.0.2 Package installation
Install and library the following packages. Let’s get started!
install.packages(c("ggplot2", "cowplot", "dplyr"))
library(ggplot2)
library(cowplot)
library(dplyr)1.0.3 The dataset
For the following examples, we will using the gapminder dataset. Gapminder is a country-year dataset with information on life expectancy, among other things.
gap = read.csv("data/gapminder-FiveYearData.csv", stringsAsFactors = TRUE)
head(gap)## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.11342 R base graphics
Base graphics are nice for quick visualizaitons of your data. You can make them publication-quality, but they take more effort than those produced by ggplot2. Let’s review base plotting calls for histograms, boxplots, and scatterplots.
2.0.1 Histogram
Histograms are useful to illustrate the distribution of a single continuous (i.e., numeric or integer) variable.
hist(x = gap$lifeExp)
# Define number of breaks
hist(x = gap$lifeExp, breaks = 5)
You can see the 657 available stock colors available to you by typing colors(). Why do you think there so many “greys”?
Change color of bars, title, x-axis label, and x and y scale limits
hist(x = gap$lifeExp,
breaks = 10,
col = "skyblue",
main = "Histogram of Life Expectancy",
xlab = "Years",
xlim = c(20, 90),
ylim = c(0, 350),
las = 1)
2.0.2 Boxplot
Boxplots are useful to visualize the distribution of a single continuous variable - which can be parsed by levels of a factor. For example, we can look at distributions of life expectancy by continent:
boxplot(gap$lifeExp ~ gap$continent,
# Give each box its own color
col = c("orange", "blue", "green", "red", "purple"))
# There are five continents represented in this dataset
levels(gap$continent)## [1] "Africa" "Americas" "Asia" "Europe" "Oceania"length(levels(gap$continent))## [1] 52.0.3 Scatterplot
Scatterplots are useful for visualizing the relationship between two continuous (i.e., numeric or integer) variables.
# Points
plot(x = gap$gdpPercap, y = gap$lifeExp, type = "p") 
# Connected lines (not a smoothing line)
plot(x = gap$gdpPercap, y = gap$lifeExp, type = "l") 
# Both
plot(x = gap$gdpPercap, y = gap$lifeExp, type = "b") 
Add a title, change the x and y axis labels and limits, change point size and shape, and map each point . Type ?pch to learn more about point shapes in base plot
# Turn off scientific notation
# options(scipen = 999)
plot(x = gap$gdpPercap, y = gap$lifeExp,
type = "p",
main = "Example scatterplot",
xlab = "GDP per capita income (USD)",
ylab = "Life Expectancy (years)",
xlim = c(0, 40000),
ylim = c(20, 90),
cex = 2, pch = 6)
3 The ggplot2 way
Base plotting is just fine, but it takes some slightly complicated code to map colors to points and shapes of each of the five continents. And, adding a legend gets even trickier. Thankfully, ggplot2 handles these complexities with ease using more compact code inspired by Leland Wilkinsons * grammar of graphics.
NOTE: ggplot2 is the name of the package, but
ggplotis the main function call.
You need three things to make a ggplot:
1. Data
2. “aes”thetics: to define your x and y axes, map colors to factor levels, etc.
3. “geom_”s: the ways to represent your data - points, bars, lines, ribbons, polygons, etc.
One thing to remember is that ggplot2 works in layers, similar to photoimaging software such as Photoshop, Illustrator, Inkscape, GIMP, ImageJ, etc. We create a base layer, and then stack layers on top of that base layer.
3.0.1 gg Histogram
3.0.1.0.1 Define the base layer
Pass in two arguments to the ggplot function to construct the base layer: the data and the global aesthetics (the ones that apply to all layers of the plot) defined within aes(). We see our coordinate system, but no data!
library(ggplot2)
ggplot(data = gap, aes(x = lifeExp))
Add your “geom_” to see the data!
ggplot(data = gap, aes(x = lifeExp)) +
geom_histogram(color = "green", fill = "orange")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Ahh, my eyes! Always avoid chartjunk! Keep your visualizations simple and crisp so that they can efficiently communicate their point without losing your audience in chartjunk.
3.0.1.0.2 theme_
theme_s in ggplot2 are the non-data parts like the background, gridlines, legends, etc. One way to improve the background of your figure is to use the theme_ layer
ggplot(data = gap, aes(x = lifeExp)) +
geom_histogram(color = "black",
fill = "gray80",
bins = 10) +
theme_bw()
Add a title and change x and y axis labels similar to before - but note the syntax differences of each layer compared to base plotting arguments from earlier
ggplot(data = gap, aes(x = lifeExp)) +
geom_histogram(color = "black",
fill = "gray80",
bins = 10) +
theme_bw() +
ggtitle("Histogram of Life Expectancy") +
xlab("Years") +
ylab("Frequency") 
We can also assign this visualization to a variable for later use
lifeExp_hist = ggplot(data = gap, aes(x = lifeExp)) +
geom_histogram(bins = 10,
fill = "green",
color = "black") +
theme_bw() +
ggtitle("Histogram of Life Expectancy") +
xlab("Years") +
ylab("Frequency")
# Call it to view
lifeExp_hist
3.0.2 gg Boxplot
ggplot boxplots are similar to base boxplots, but the helpful additions and customizations are easier to understand and define. Make boxplots of lifeExp for the five continents. What has fill = continent done!?
What do you think is the difference between fill and color?
ggplot(data = gap, aes(x = continent, y = lifeExp, fill = continent)) +
geom_boxplot() +
theme_minimal()
3.0.3 gg Scatterplot
ggplot scatterplots are again similar to base scatterplots, again with the ease of feature customization. Make a scatterplot of lifeExp by gdpPercap - what has color = continent done!?
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point() +
theme_test()
The legend can be moved around by adding the legend.position argument to the theme layer
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point() +
theme(legend.position = "top")
This is also helpful for manipulating the text of the axis labels
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point() +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) 
3.0.3.0.1 Custom scale breaks
To set custom breaks we use a different layer. To create a custom scale that goes from a start point to some end point by some interval
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point() +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) +
scale_y_continuous(breaks = seq(from = 20, to = 90, by = 10), limits = c(20, 90))
3.0.3.0.2 Point sizes, shapes, transparencies
Change point sizes, shapes, and transparencies
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp,
color = continent,
size = 2,
shape = continent)) +
geom_point(alpha = 0.25) +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) +
scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90))
3.0.3.0.3 Log transform axis
Alternatively, you can log transform an axis as well …
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp,
color = continent,
size = 2,
shape = continent)) +
geom_point(alpha = 0.25) +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) +
# scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) +
scale_x_log10() +
scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90))
… and add smoothing lines
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp,
color = continent,
size = 2,
shape = continent)) +
geom_point(alpha = 0.25) +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) +
# scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) +
scale_x_log10() +
scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90)) +
geom_smooth(method = "lm", se = TRUE, lwd = 1)
Save as a variable for later …
gdpLe_scatter = ggplot(data = gap, aes(x = gdpPercap, y = lifeExp,
color = continent,
size = 2,
shape = continent)) +
geom_point(alpha = 0.25) +
theme_bw() + # Does it still work if you add this theme after the other theme?
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)) +
# scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) +
scale_x_log10() +
scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90)) +
geom_smooth(method = "lm", se = TRUE, lwd = 1)
gdpLe_scatter
3.0.4 gg Lineplot
Lineplots are useful for visualizing change in some variable on the y-axis plotted against time on the x-axis. There are many different ways to do this, including reshaping your data using the reshape2 or tidyr packages.
We will look at a quick dplyr review to add a column to our gap dataset of the mean lifeExp for each continent by year. Check out D-Lab’s Data Wrangling and Manipulation in R to learn more!
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongap_lifeExp_mean = gap %>%
group_by(year, continent) %>%
mutate(mean_lifeExp = mean(lifeExp))
head(gap_lifeExp_mean)## # A tibble: 6 x 7
## # Groups: year, continent [6]
## country year pop continent lifeExp gdpPercap mean_lifeExp
## <fct> <int> <dbl> <fct> <dbl> <dbl> <dbl>
## 1 Afghanistan 1952 8425333 Asia 28.8 779. 46.3
## 2 Afghanistan 1957 9240934 Asia 30.3 821. 49.3
## 3 Afghanistan 1962 10267083 Asia 32.0 853. 51.6
## 4 Afghanistan 1967 11537966 Asia 34.0 836. 54.7
## 5 Afghanistan 1972 13079460 Asia 36.1 740. 57.3
## 6 Afghanistan 1977 14880372 Asia 38.4 786. 59.6Plot!
ggplot(gap_lifeExp_mean, aes(x = year, y = mean_lifeExp,
color = continent,
linetype = continent)) +
geom_line(lwd = 2) +
theme_bw() +
theme(legend.position = "top")
Increase legend size using theme and change legend title
ggplot(gap_lifeExp_mean, aes(x = year, y = mean_lifeExp,
color = continent,
linetype = continent)) +
geom_line(lwd = 2) +
theme_bw() +
theme(legend.position = "top",
legend.title = element_text(color = "black", size = 12, face = "bold"),
legend.text = element_text(color = "black", size = 12, face = "bold")) +
guides(color = guide_legend(title = "PIZZA"),
linetype = FALSE)
Or: - remove the legend title
- increase the size of the legend lines
- increase the spacing of the legend items
- right align the legend text
- move labels to left of glyphs
- save as a variable
gap_line = ggplot(gap_lifeExp_mean, aes(x = year, y = mean_lifeExp,
color = continent,
linetype = continent)) +
geom_line(lwd = 2) +
theme_bw() +
theme(legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(color = "black", size = 10, face = "bold"),
legend.key.width = unit(2.54, "cm"),
legend.text.align = 1,
legend.key = element_rect(size = 3, fill = "white", colour = NA), legend.key.size = unit(1, "cm")) +
guides(color = guide_legend(label.position = "left"))
gap_line
3.0.4.0.1 Faceting
You can also facet your plots to turn overlaid figures into separate ones. For example:
gap_line = gap_line +
facet_wrap(vars(continent)) +
guides(linetype = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
gap_line
3.0.5 gg Heatmap
Heatmaps are useful when you want to plot three variables - one continuous variable by two factors.
- Add a gradient scale fill based on lifeExp (darker colors represent more years)
- Put years to left of legend glyphs
- Save it as a varialbe
heat = ggplot(gap, aes(x = continent, y = year, fill = lifeExp)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "gray20",
limits = c(20,90), breaks = seq(20, 90, 10)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_y_continuous(breaks = seq(from = 1952, to = 2007, by = 5), limits = c(1947, 2012)) +
guides(fill = guide_colourbar(label.position = "left"))
heat
4 cowplot for compound figures!
Combine figures into a single compound figure
library(cowplot)##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************compound = plot_grid(lifeExp_hist, gdpLe_scatter, gap_line, heat,
nrow = 2, ncol = 2,
scale = 0.85,
labels = c("A)", "B)", "C)", "D)"))
compound
5 Exporting
Exporting graphs in R is straightforward. Start by clicking the “Export” button:
1. Click Copy to clipboard… if you want to quickly copy/paste a figure into a slideshow presentation or text document
- Click Save as image… (Raster/Bitmap formats such as .png, .jpeg, .tiff) if you want to explort to this format
NOTE: Not recommended because every pixel of a plot contains its own separate coding; not so great if you want to resize the image
- Click Save as PDF… (Vector format such as .pdf, .ps) to export to .pdf.
NOTE: Recommended! Every element of a plot is encoded with a function that gives its coding conditional on several factors; great for resizing
- Export to .pdf
- Open this .pdf file and click File –> Export As
- Define resolution
- Save as .tiff format (with .jpeg compression is applicable)
- This allows you to maintain resolution while shrinking the file size!
- Or, export with
ggsave
# Assume we saved our plot is an object called example.plot
ggsave(filename = "visuals/compound.pdf", plot = compound,
width = 12, height = 8, units = "in", dpi = 600)